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FDEOS

FDEOS is a complete client-engagement methodology for you—the human Forward Deployed Engineer on site—working with an AI coding agent (e.g. Claude Code). It is built from composable skills the AI loads plus .fde/ memory the AI reads and updates so each session starts where you left off—not from zero.

You (human) describe what is happening on the engagement. You type @fde in the AI’s chat. The AI routes to the right phase (land, discover, plan, build, rescue, close). You do not memorize skill names.

Built across 100+ embeds (enterprise, startup, regulated, rescue), mostly solo on site. Open source so you do not relearn the same political and delivery mistakes.

Subash Natarajan


Who does what

Role Who What they do
You (the FDE) Human on the engagement Stakeholder meetings, calls, judgment, sign-off, typing @fde, owning what ships
AI coding agent Software on your laptop (e.g. Claude Code, Cursor with skills) — not a human colleague Loads FDEOS skills, reads/updates .fde/, drafts code and docs in your workspace
@fde Skill inside the AI agent Routes your message to the right engagement phase

In this repo, “agent” always means the AI coding agent, never a person (not the client’s team, not a forward-deployed human peer).

  YOU (human FDE)                    AI CODING AGENT (software)
  meetings, judgment        @fde      skills + .fde/ memory
        │                 ─────►            │
        │                                   ▼
        └──────────────►  client workspace (code, VPN, tickets)

Quickstart

Give your AI coding agent FDEOS: Claude Code (primary). Any tool that loads ~/.claude/skills/ can use the same files.

  1. Install plugin + copy skills (below)
  2. node bin/install.js init my-engagement (from repo clone)
  3. Set FDEOS_ENGAGEMENT=~/fde-engagements/my-engagement/.fde
  4. Open your workspace (repo, VPN, tickets—whatever access you already have)
  5. In the AI chat, type: @fde I'm on site. First stakeholder meeting tomorrow. Brief says fix the payments API.

Deeper paths: docs/install.md · Example arc: examples/acme-payments/


How it works

It starts when you open a session and message the AI coding agent about the engagement.

The AI does not treat the written brief as truth and jump into implementation. It loads context.md from your private engagement folder—where you left off. You say what changed today (meeting, outage, quiet sponsor, wrong scope). @fde tells the AI whether you are landing, discovering, planning, building, rescuing, or closing, and which skills and .fde/ files to use.

While you work together, the AI should update that folder: who matters in stakeholders.md, what is actually broken in reality.md, what you agreed in decisions.md, what shipped in delivery.md. Next morning you are not re-pasting context into a blank chat.

Layer Where it lives Who touches it
Skills Your machine: ~/.claude/skills/ AI loads them; you install once
Engagement notes ~/fde-engagements/<name>/.fde/ AI reads/writes; you own the folder
Code & delivery Client workspace you already use You + AI; FDEOS never installs on client servers by default
  your laptop                         client work (your access)
  ┌────────────────────────┐          ┌────────────────────────┐
  │ Human: you (FDE)       │          │ Meetings, repo, prod   │
  │ AI: @fde + skills      │  @fde    │                        │
  │ ~/fde-engagements/…    │ ───────► │                        │
  └────────────────────────┘          └────────────────────────┘

Entry skill: @fde (skills/fde/SKILL.md). Other skills are reached through it.


Who this is for

You are… FDEOS helps when…
New to embed work You need a clear first week before you touch production
Senior operator, solo on site Your AI coding agent is your only backup for judgment + memory + delivery between meetings
Multi-client consultant You must keep each engagement’s context separate

Requirements: An AI coding agent that loads skills (Claude Code is what I use daily — install).

Not for: Teams who only need generic coding help in one repo, with no client politics, memory, or embed lifecycle.


Without FDEOS vs with FDEOS

Without FDEOS With FDEOS
AI’s role Strong coder Coder + engagement partner
Memory You re-paste context every session context.md + .fde/ trail the AI reloads
Brief AI treats it as the task AI treats it as hypothesis until discover
Routing You pick tools and prompts @fde picks phase and skills
Risk Right code, wrong problem or wrong room Aims at alignment before big builds

FDEOS does not get you access, approvals, or compliance sign-off. It structures how you (human) and your AI coding agent run the embed.


Installation

Install FDEOS once per AI tool if you use more than one (e.g. Claude Code and another editor with skills).

Claude Code

FDEOS is on the fde-os marketplace:

/plugin marketplace add suboss87/fde-os
/plugin install fdeos@fdeos

Copy skills and hooks to disk (once per machine):

git clone https://github.com/suboss87/fde-os.git
cd fde-os && node bin/install.js

When npm shows 2.1.0+: npx fdeos@latest (registry may lag—git clone is always current).

Per engagement:

node bin/install.js init <engagement-name>

Tell the AI where notes live — in ~/.claude/FDEOS-CLAUDE.md or your shell:

FDEOS_ENGAGEMENT=~/fde-engagements/<engagement-name>/.fde

Full matrix: docs/install.md


The basic workflow

You only type @fde in the AI chat. The AI coding agent checks your situation and activates the matching skill.

  1. land — Day one. Stakeholders, brief as hypothesis, success and trust before code.

  2. discover — Reality ≠ brief. Shadow work, real problem, terrain in reality.md / terrain.md.

  3. plan — Sequence the next slices. PR-sized tasks, risks, cadence in decisions.md / risks.md.

  4. build — Agreed slice. Legacy-safe implementation, characterization where fragile.

  5. review — Before merge. Scope first, then safety (@fde-review).

  6. ship — Production. Pre-flight, canary, rollback (@fde-ship).

  7. close — Handoff. What the client team runs without you (handoff.md, patterns.md).

Also routes to

When Skill
Production broken or trust crisis @fde-rescue
Taking over half-done work @fde-audit
Prove direction before big build @fde-sketch
Several clients at once @fde-dashboard (one init per client)

Sample filled files (fictional): examples/acme-payments/


What's inside

Skills are instructions the AI coding agent loads. You invoke only @fde.

Entry

  • @fde — Hear the situation; route silently to the right phase

Engagement

  • @fde-land — First 48 hours: trust, stakeholders, success criteria
  • @fde-discover — Real problem vs brief; shadow work; map the codebase
  • @fde-audit — Mid-engagement takeover
  • @fde-rescue — Outage or sponsor/trust crisis
  • @fde-sketch — Prototype to test direction
  • @fde-close — Handoff and patterns

Execution

  • @fde-engineering — Build → cleanup → review → verify loop
  • @fde-plan — Atomic tasks, stakeholder touchpoints
  • @fde-build — Brownfield-safe implementation
  • @fde-review — Scope first, then safety
  • @fde-debug — Reproduce, isolate, fix
  • @fde-ship — Deploy with rollback

Visibility

  • @fde-dashboard — Status across ~/fde-engagements/

Regulated overlays

  • healthcare-fde, fintech-fde, gov-fde — Extra judgment for PHI, payments, or government data (operational guardrails, not legal advice)

Full list: docs/skills-reference.md


Engagement memory (.fde/)

Default: ~/fde-engagements/<name>/.fde/ via FDEOS_ENGAGEMENT. Private to you; the AI reads/writes; client infra does not host this by default.

File Role
context.md Where you are; AI loads first every session
brief.md What they said—hypothesis until discover
reality.md What is actually true
stakeholders.md Champions, resistance, trust signals
decisions.md Plan and technical choices
delivery.md What shipped

Do not commit client-identifying content to shared git.

Schema: docs/schema.md · Day-to-day: docs/USAGE.md


Principles

  • Trust before production — You earn the right to touch their systems
  • Brief is a hypothesis — Discover before the AI builds the wrong thing
  • Map before moving — Unknown terrain gets characterization, not drive-by refactors
  • Thin slices — Ship learning, not theatre
  • Evidence over claims — Verify before you or the AI say it is fixed or done
  • Engagement memory — Write it down so the next session is not day one again

Full methodology: FDE-METHODOLOGY.md


Updating

cd fde-os && git pull && node bin/install.js

Plugin updates follow Claude Code marketplace refresh. Re-run init only for new engagements—existing ~/fde-engagements/ folders are preserved.


Contributing

Maintained by Subash Natarajan. Feedback via Issues — see CONTRIBUTING.md.

ATTRIBUTION.md · SECURITY.md · Repo layout · MIT

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The Operating System for Forward Deployed Engineering. 14 skills for the complete FDE lifecycle.

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